MRI Image Reconstruction Using Encoding Matrix Modeling
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Solution Overview
Problem
Existing magnetic resonance imaging (MRI) systems face challenges in efficiently reconstructing images due to limitations in modeling all components of the system, requiring dense sampling, and taking hours to produce high-quality images.
Innovation Solution
The proposed solution involves a method and system for reconstructing MRI images by generating an encoding matrix that models all or nearly all components of the MRI system, including magnetic field gradients, coil sensitivities, and gradient profiles, allowing for compressibility and optimization to expedite the reconstruction process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional MRI systems acquire samples on a rectilinear grid and use 2D inverse FFT for reconstruction, then the reconstruction process is simple and fast, but the system cannot model complex MRI components and requires dense sampling which increases data acquisition time
Solution Approach 1:
The patent changes the fundamental parameter of data representation from simple Fourier space sampling to encoding matrix-based representation. By transforming the reconstruction approach from direct 2D inverse FFT to solving a linear system with an encoding matrix that models all MRI components, the system achieves both high modeling accuracy and computational efficiency through optimized linear algebra operations.
Solution Approach 2:
The encoding matrix serves multiple functions simultaneously: it models magnetic field gradients, coil sensitivities, gradient profiles, and other MRI system components in a unified framework. This universal representation allows the same mathematical structure to handle diverse physical phenomena that would otherwise require separate modeling approaches.
2Measurement precision
If MRI systems model all components including magnetic field gradients, coil sensitivities, and gradient profiles, then the modeling accuracy improves, but the computational complexity and processing time increase significantly
Solution Approach 1:
The patent segments the complex MRI system into distinct modular components, each represented by a separate matrix or vector (e.g., gradient encoding matrix, coil sensitivity matrix, gradient profile vectors). These segmented components are then combined through matrix multiplication to form the complete encoding matrix, making the overall system manageable and computationally efficient.
Solution Approach 2:
The patent replaces traditional mechanical signal processing approaches with a mathematical substitution using linear algebra. Instead of physically processing signals through multiple sequential steps, the system uses matrix operations to simultaneously represent and process all MRI components, dramatically reducing computational complexity.
3Loss of information
If dense sampling is used in traditional MRI, then complete image information is captured, but the data acquisition time and processing burden increase
Solution Approach 1:
The patent applies partial sampling strategies where only a subset of k-space data is acquired, leveraging the encoding matrix model to reconstruct the complete image information. The mathematical model compensates for the missing data, allowing high-quality reconstruction with fewer measurements than traditional dense sampling would require.
Solution Approach 2:
The encoding matrix acts as an intermediary that bridges the gap between partial measurements and complete image reconstruction. This mathematical mediator translates sparse sampling data into comprehensive image information by incorporating prior knowledge about MRI system physics embedded in the encoding matrix structure.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables robust and efficient image reconstruction that can model complex MRI system components, achieve compressibility for faster processing, and produce high-quality images in minutes, addressing the limitations of existing methods.
Implementation Method 1
a total magnetic field including magnetic field gradients and one or more RF pulse sequences may be applied using a transmit coil and a radio frequency (RF) source, respectively
Implementation Method 2
receiving, from one or more receive coils, MRI measurement data acquired by the receive coils during an acquisition window, the MRI measurement data including magnetic resonance signal data emitted by the sample
Data Source
AI summary
Some embodiments of the present disclosure disclose systems and methods for robust magnetic resonance image reconstruction that can model for all or nearly all components in the magnetic resonance imaging system, that possess compressibility features to speed up reconstructions, and that can be optimized such that the reconstruction can be performed within a short period of time.